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The overall performance of joint models for SF and IC (Table REF ) is competitive with independent models (Table REF ). The advantage of joint models is that they have relatively less parameters than independent models, as both tasks are trained on a single model.

When computational power is not an issue, fine-tuning a pre-trained model such as BERT is the way to go for maximum SF and IC performance. Hybrid methods combining parameter and state sharing + intent gating yield the best performance [1]}.

For the non BERT-based model, using state sharing [2]} is the best on ATIS. However, the disadvantage is that it is actually a bi-model and not a single model.

Similar to independent models, contextual information is crucial to performance. Adding a self-attention mechanism [3]}, [4]} to either parameter and state sharing or to slot-intent gating can boost performance even further.

When sufficiently large in-domain training data is available, the SF and IC performance in ATIS and SNIPS is already saturated. Therefore, further research on this classic leaderboard chase is not worth it. We discuss more about that in Section 6.

Most of the work in joint models and also independent models (Section §) reports F1 scores for slot filling performance. However, these scores do not reveal in which specific cases these models behave differently, contributing to overall performance. We leave further analysis on model performance as a potential future work.

